Multi-view Bi-clustering to Identify Smartphone Sensing Features Indicative of Depression

Abstract

Depression is a major public health issue with direct and significant effects on both physical and mental health. In this study, we analyze smartphone sensing data to find differential behavioral features that are correlated with depression measures such as patient health questionnaire (PHQ-9). Our approach uses an innovative multi-view bi-clustering algorithm. It takes multiple views of sensing data as input to identify homogeneous behavioral groups and simultaneously the key sensing features that characterize the different groups. Using a publicly available dataset, we discover that these behavioral groups with differential sensing features are highly discriminative of PHQ-9 scores that are self reported by the study subjects. For instance, the group comprising less active users in the sensed activities corresponds to overall higher PHQ-9 scores. We then employ the key sensing features that distinguish the different groups to create predictive models to predict the group assignment of individuals. We verify the generalizability of these models using the support vector machine classifier. Cross validation studies show that our classifiers can classify individuals into the correct subgroups with an overall accuracy of 87%.